Non-invasive peripheral nerve conduction studies (NCS) are an important tool in the diagnosis and assessment of neuromuscular injuries and pathologies. Electrical stimulation of a nerve bundle by surface electrodes produces impulses that travel in both the proximal and distal directions. Compound signals can be differentially recorded from the muscle or muscle group that is innervated by the stimulated nerve or from a separate location over the nerve itself. The amplitude and latency (or conduction velocity) of these evoked potential (EP) signals are calculated and used clinically to determine the location of nerve lesions and/or to provide an overall characterization of nerve function. More elaborate analysis of both compound muscle action potentials (CMAPs) and sensory nerve action potentials (SNAPs) have also been investigated and are believed to provide more precise diagnoses and assessment by extracting additional information from the complex signals.
Large artifacts due to the electrical stimuli often appear in surface EP traces. Stimulus artifacts can be significant enough in magnitude and duration to contaminate the CMAP or SNAP waveform. Signal contamination can be severe, particularly in SNAP recordings where the evoked potential may only be a few microvolts in amplitude. The causes of stimulus artifacts include actual voltage gradients between the recording electrodes, capacitive coupling between the stimulation and detection hardware, and shaping of the stimulus spike by the detection amplifier and analog filters. The magnitude of the artifacts can generally be reduced through careful hardware design, improved skin preparation, and the use of sample-and-hold amplifiers or delay circuits that exclude the stimulus from the recorded action potential trace. In general, however, the stimulus artifacts cannot be completely eliminated from peripheral evoked potential recordings and may dwarf the EP waveform even after implementing these measures.
Several methods of post-processing to remove stimulus artifacts from EP recordings have been investigated and documented. Inverse filtering to counteract the effects of the detection amplifier, fitting of an artifact to a parameterized function, estimation and subtraction of an artifact from a separate recording, a sub-threshold stimulus or a second stimulus pulse during the refractory period, and non-linear adaptive filtering techniques have all been used. While these methods have proven effective and useful, none are universally applicable and the search for new methods for stimulus artifact removal continues.
Another aspect that complicates evoked potential analysis is the compound nature of the recorded signals. Often, healthy and diseased tissues are both present and are both activated and recorded. The response of healthy tissue, having a normal amplitude and latency, can mask the effect of existing pathology. Ideally, the healthy and diseased tissues could be measured separately, but this is very difficult in practice. Alternatively, it would be useful to be able to separate the healthy and diseases responses from compound signals that contain both.
Independent component analysis (ICA) is a statistical analysis method that has applications in telecommunications, image processing, and biomedical signal analysis. ICA identifies and extracts the contributions of different, non-Gaussian sources given multiple recordings that are linear mixtures of those contributions. The mixtures may be of multiple sources of interest, in which case ICA allows tracking of the amplitude and latency of each separated source, or they may include unwanted signals such as artifacts that can, after being identified with ICA, be removed from the recordings. Often referred to as a method of blind source separation (BSS), ICA can be performed with no a-priori knowledge of the source signals other than their statistical independence, and no a-priori knowledge about the contribution of each source signal to the recorded mixtures. ICA can also be performed when limited knowledge is available or assumed about either the morphology of the source signals or their contributions to the recordings. Several ICA algorithms have been recently developed, including a fast ICA (FICA) Matlab package that is freely available on the World Wide Web.
In biomedical signal analysis, ICA has been used very promisingly in the separation of multiple sources in scalp recordings of somato-sensory, visual, or auditory evoked potentials (SEP, VEP, AEP) or for source separation and the removal of motion and eye-blink artifacts in passive electroencephalography (EEG). These applications lend themselves to ICA because they involve numerous detection electrodes recording combinations of sources from a relatively large distance. The effects of source propagation and of dissimilar filtering by the intervening tissues are neglected for this far-field recording situation and all sources are assumed to contribute an identical, but scaled, component to each recording.
This is not usually the case for peripheral EPs, which are recorded in closer proximity to a larger, more coherent group of sources. Different electrode locations over an activated muscle will produce CMAPs that differ in shape and temporal extent due to the active propagation of the generating sources and their near-field relationship to the detection electrodes. Similarly, even closely spaced detection sites along a nerve will see SNAPs that have different latencies and durations due to propagation of the sources past the electrodes and temporal dispersion among the individual action potentials that compose the compound SNAPs.
While independent component analysis (ICA) appears to be a very useful tool for blind source separation and removal of contaminating artifacts from cortical evoked potential and EEG recordings, spatially separated peripheral compound muscle and sensory nerve action potentials do not fit the model of linear mixtures normally required for ICA.